Unit for and method of motion estimation and image processing apparatus provided with such motion estimation unit

ABSTRACT

The motion estimation unit ( 100 ) comprises a first summation means ( 106 ) for calculating match errors of a number of candidate motion vectors of a segment ( 116 ) of a first image ( 118 ). The motion vector with the lowest match error can be assigned to the segment ( 116 ) as the estimated motion vector. The match error is based on summation of absolute differences between pixel values of the segment ( 116 ) and pixel values of a second image ( 120 ). In order to estimate the deviation of the estimated motion from the true motion vector, i.e. the motion vector error ( 130 ), the motion estimation unit ( 100 ) further comprises a second summation means ( 108 ) for calculating a variance parameter by summation of absolute differences between pixel values of the segment ( 116 ) and pixel values of the first image ( 118 ) and estimation means ( 110 ) for estimating a motion vector error ( 130 ) by comparing the match error with the variance parameter.

[0001] The invention relates to a motion estimation unit for generatingmotion vectors, each corresponding to a respective segment of a firstimage, comprising a first summation means for calculating a match errorof a motion vector of the segment by summation of absolute differencesbetween pixel values of the segment and pixel values of a second image.

[0002] The invention further relates to a method of generating motionvectors, each corresponding to a respective segment of a first image,comprising a first summation step of calculating a match error of amotion vector of the segment by summation of absolute differencesbetween pixel values of the segment and pixel values of a second image.

[0003] The invention further relates to an image processing apparatuscomprising:

[0004] a motion estimation unit for generating motion vectors, eachcorresponding to a respective segment of a first image, comprising afirst summation means for calculating a match error of a motion vectorof the segment by summation of absolute differences between pixel valuesof the segment and pixel values of a second image; and

[0005] a motion compensated image processing unit.

[0006] An embodiment of the method of the kind described in the openingparagraph is known from the article “True-Motion Estimation with 3-DRecursive Search Block Matching” by G. de Haan et. al. in IEEETransactions on circuits and systems for video technology, vol. 3, no.5, October 1993, pages 368-379.

[0007] For many applications in video signal processing, it is necessaryto know the apparent velocity field of a sequence of images, known asthe optical flow. This optical flow is given as a time-varying vectorfield, i.e., one vector field per image. In the cited article thismotion vector field is estimated by dividing the image into blocks. Fora set of candidate motion vectors of each block a match error iscalculated and used in a minimization procedure to find the mostappropriate motion vector from the set of candidate motion vectors ofthe block. The match error corresponds to the SAD: sum of absoluteluminance differences between pixels in a block of an image, and thepixels of a block in the next image shifted by the motion vector:$\begin{matrix}{{{SAD}\left( {x,y,d_{x},d_{y},n} \right)}:={\sum\limits_{i = 0}^{N}\quad {\sum\limits_{j = 0}^{M}\quad {{{Y\left( {{x + i},{y + j},n} \right)} - {Y\left( {{x + d_{x} + i},{y + d_{y} + j},{n + 1}} \right)}}}}}} & (1)\end{matrix}$

[0008] Here (x,y) is the position of the block, (d_(x), d_(y)) is amotion vector, n is the image number, N and M are the width and heightof the block, and Y(x,y,n) is the value of the luminance of a pixel atposition (x,y) in image n.

[0009] The estimated motion vector may deviate from the true motionvector, i.e. there may be a motion vector error. The value of SAD canonly be used in comparisons, as a relative measure. The calculated SADdoes not give a reliable indication of the accuracy of the motionvector, i.e. the motion vector error. This is a drawback of the methodaccording to the prior art.

[0010] It is a first object of the invention to provide a motionestimation unit of the kind described in the opening paragraph which isarranged to calculate a motion vector error.

[0011] It is a second object of the invention to provide a method of thekind described in the opening paragraph in which a motion vector erroris calculated.

[0012] It is a third object of the invention to provide an imageprocessing apparatus of the kind described in the opening paragraphwhich is arranged to calculate a motion vector error.

[0013] The first object of the invention is achieved in that the motionestimation unit further comprises:

[0014] a second summation means for calculating a variance parameter bysummation of absolute differences between pixel values of the segmentand pixel values of the first image; and

[0015] an estimation means for estimating a motion vector error bycomparing the match error with the variance parameter.

[0016] With the second summation means a variance parameter can becalculated which is a measure of the level of detail in the block. Ablock is a type of segment. Motion estimation for irregular shapedsegments is also quite common. The invention is also applicable toirregular shaped segments. If there is much detail in the block, i.e.resulting in a relatively high value of the variance parameter, then thevalue of the match error can have a relatively high value too. Arelatively low value of the variance parameter in combination with arelatively high value of the match error indicates that the estimatedmotion vector might be erroneous.

[0017] It is an advantage of the motion estimation unit according to theinvention that the motion estimation unit provides a motion vectorerror, because there are applications where one is interested in thevalue of the motion vector error. For instance, when the motion vectoris good enough, it is unnecessary to spend time on trying to improve it.Efficiency and vector field consistency are thereby improved. A motionvector error can also be useful for estimating the global quality of themotion vector field, to decide when to use fall-back options, or moresophisticated algorithms that generate fewer artifacts. It can also beused to find blocks that have an overlap with object edges, which canthen be handled in a more suitable way.

[0018] A related idea has been proposed in EP 0 549 681 B2 in which amotion estimator unit is described which operates on at least fourimages to determine a motion vector. In that patent specification it isproposed to estimate motion vectors by minimization of assignment errorswhich are calculated by dividing a difference between values of pixelsof subsequent images by a so-called gradient. This gradient iscalculated by taking the square root of quadratic differences. Thefollowing major differences between the motion estimation unit accordingto the invention and the motion estimation unit described in EP 0 549681 B2 are:

[0019] The variance parameter and the match error of the motionestimation unit according to the invention are based on an equivalentmeasure: a sum of absolute differences. In the motion estimation unitdescribed in EP 0 549 681 B2 two different measures are calculated: adifferences and a square root of quadratic differences. An advantages ofusing twice a sum of absolute differences is that the motion vectorerror can provide qualitative and even quantitative information aboutthe deviation of the estimated motion vector from the true motionvector. Experiments to prove this have been performed. The results ofthese experiments are described hereinafter in connection with the FIGS.3 to FIG. 7. Another advantages of using twice a sum of absolutedifferences is that it is less computation intensive.

[0020] The variance parameter might be used in the motion estimationunit according to the invention for normalization of the match errors ofall candidate motion vectors. However this is not necessary because thevariance parameter is a constant for all candidate motion vectors of asegment. It is sufficient if only for the estimated motion vector, i.e.one out of the set of candidate motion vectors, the motion vector erroris calculated in order to determine whether the estimated motion vectormight be an appropriate value of the true motion. In the motionestimation unit described in EP 0 549 681 B2 a normalization isperformed for all candidate motion vectors.

[0021] In an embodiment of the motion estimation unit according to theinvention, the second summation means is designed to calculate thevariance parameter by adding:

[0022] absolute differences between pixel values of the segment andpixel values of a second segment which corresponds to the segment beingshifted at least one pixel in a first direction; and

[0023] absolute differences between pixel values of the segment andpixel values of a third segment which corresponds to the segment beingshifted at least one pixel in a second direction,

[0024] the first direction being cross to the second direction.

[0025] The purpose of the variance parameter measure is to predict thesum of absolute differences resulting from a known motion vectordeviation. Since the true motion vector is not known, a segment ismatched against the slightly displaced, i.e. shifted, segment in thesame image. As the direction of the error is not known, an average overtwo directions is made. In other words, by comparing two measures, i.e.the match error with the variance parameter, which are both a measure ofa translation, it is possible to derive the motion vector error which isrelated to the difference between the estimated motion vector, and thetrue motion vector. The match error is based on a translation of a blockbetween subsequent images, and the variance parameter is based ontranslation of a block within one image. The value of the shift appliedto the block for calculating the variance parameter is stochasticallyrelated to the variance parameter. The probability that, if the shiftapplied to the block for calculating the variance parameter isincreased, the variance parameter will increase too, is relativelylarge. With the help of the variance parameter, a statement can be maderegarding the distribution of the match error values as a function ofthe motion vector error.

[0026] In a modification of the embodiment of the motion estimation unitaccording to the invention, the second summation means is designed toadd also absolute differences between pixel values of the segment andpixel values of a fourth segment which corresponds to the segment beingshifted at least one pixel in the first direction and at least one pixelin the second direction. The advantage of this embodiment is that alsodirections which are diagonal on the first and second direction aretaken into account when the motion estimation unit is in operation.

[0027] An embodiment of the motion estimation unit according to theinvention is designed to generate the motion vector of the segment beinga block of pixels. An advantage of a motion estimation unit based onblocks of pixels is its simplicity of design. Another embodiment of themotion estimation unit according to the invention is designed to handleirregularly shaped segments.

[0028] An embodiment of the motion estimation unit according to theinvention is designed to generate the motion vector of the segment basedon luminance values as pixel values. Luminance is an appropriatequantity for motion estimation. Another embodiment of the motionestimation unit according to the invention is designed to operate onchrominance values.

[0029] The second object of the invention is achieved in that the methodfurther comprises:

[0030] a second summation step of calculating a variance parameter bysummation of absolute differences between pixel values of the segmentand pixel values of the first image; and

[0031] an estimation step of estimating a motion vector error bycomparing the match error with the variance parameter.

[0032] The third object of the invention is achieved in that the motionestimation unit of the image processing apparatus further comprises:

[0033] a second summation means for calculating a variance parameter bysummation of absolute differences between pixel values of the segmentand pixel values of the first image; and

[0034] an estimation means for estimating a motion vector error bycomparing the match error with the variance parameter.

[0035] Modifications of the image processing apparatus and variationsthereof may correspond to modifications and variations thereof of themotion estimator unit described. The image processing apparatus maycomprise additional components, e.g. receiving means for receiving asignal representing images and a display device for displaying theprocessed images. The motion compensated image processing unit mightsupport one or more of the following types of image processing:

[0036] De-interlacing: Interlacing is the common video broadcastprocedure for transmitting the odd or even numbered image linesalternately. De-interlacing attempts to restore the full verticalresolution, i.e. make odd and even lines available simultaneously foreach image;

[0037] Up-conversion: From a series of original input images a largerseries of output images is calculated. Output images are temporallylocated between two original input images; and

[0038] Temporal noise reduction.

[0039] These and other aspects of the motion estimation unit, of themethod and of the image processing apparatus according to the inventionwill become apparent from and will be elucidated with respect to theimplementations and embodiments described hereinafter and with referenceto the accompanying drawings, wherein:

[0040]FIG. 1 schematically shows an embodiment of the motion estimationunit;

[0041]FIG. 2 schematically shows an embodiment of the image processingapparatus;

[0042]FIG. 3 shows a histogram of SAD values for a test sequence used inthe experiments for fixed VAR of 1500, and VE of 1 pixel, together withbest Gaussian fit;

[0043]FIG. 4A shows the dependence of μ as function of VAR for VE=0, fora test sequence;

[0044]FIG. 4B shows the dependence of σ as function of VAR for VE=0, fora test sequence;

[0045]FIG. 5A shows the dependence of μ as function of VAR for VE=1, fora test sequence;

[0046]FIG. 5B shows the dependence of σ as function of VAR for VE=1, fora test sequence;

[0047]FIG. 6 shows the dependence of constant a in SAD≈

(αVAR,αVAR,3.0) on VE, for a test sequence; and

[0048]FIG. 7 shows plots of P(VE=e|SAD=x, VAR=v) as function of e forvarious values of x/v.

[0049] Corresponding reference numerals have the same meaning in all ofthe Figures.

[0050]FIG. 1 schematically shows an embodiment of the motion estimationunit 100, comprising:

[0051] a generating means 102 for generating a set of candidate motionvectors of a segment 116 of a first image 118;

[0052] a first summation means 106 for calculating match errors ofcandidate motion vectors of segment 116 by summation of absolutedifferences between pixel values of the segment 116 and pixel values ofa second image 120;

[0053] a second summation means 108 for calculating a variance parameterby summation of absolute differences between pixel values of the segment116 and pixel values of the first image 118;

[0054] an estimation means 110 for estimating a motion vector error 130by comparing a particular match error with the variance parameter; and

[0055] a selecting means 104 for selecting a particular motion vector126 from the set of candidate motion vectors on the basis of the matcherrors.

[0056] The input of the motion estimator unit 100 comprises images andis provided at an input connector 112. The output of the motionestimator unit 100 are motion vector fields, e.g. 124 comprising motionvectors of the segments, e.g. 116. The output of the motion estimatorunit 100 is provided at an output connector 114. The behavior of themotion estimator unit 100 is as follows. First the generating means 102generates for a segment 116, e.g. block of pixels, a set of candidatemotion vectors. Then the first summation means 106 calculates for thesecandidate motion vectors the match errors. Then the selecting means 104selects a particular motion vector 126 from the set of candidate motionvectors on the basis of these match errors. This particular motionvector 126 is selected because its match error has the lowest value. Thesecond summation means 108 calculates the variance parameter of thesegment 116. For the particular motion vector 126 and optionally forsome other motion vectors of the candidate set the motion vector errors,e.g. 130, are calculated based on the match errors and the varianceparameter. A motion vector error, e.g. 130 corresponds to the length ofthe difference vector between the true motion vector 128 and the motionvector 126 used in calculating the match error. These motion vectorerrors, e.g. 130 are input for the selecting means 104. If the value ofthe motion vector error 130 of the particular motion vector 126 is lessthan a predetermined threshold then the particular motion vector 126 isassigned to the segment 116. The selecting means 104 is arranged totrigger the generating means 102 to generate a new set of candidatemotion vectors if the value of the motion vector error 130 of theparticular motion vector 126 is higher then the predetermined threshold.The selecting means 104 is informed by the estimation means 110 whetherthe motion vectors for which the motion vector errors have beencalculated too, might be appropriate candidates for other segments.

[0057] The match error being calculated by the first summation means 106corresponds to the SAD: sum of absolute luminance differences betweenpixels in a particular block of an image, and the pixels of a block inthe next image corresponding to the particular block shifted by themotion vector: $\begin{matrix}{{{SAD}\left( {x,y,d_{x},d_{y},n} \right)}:={\sum\limits_{i = 0}^{N}\quad {\sum\limits_{j = 0}^{M}\quad {{{Y\left( {{x + i},{y + j},n} \right)} - {Y\left( {{x + d_{x} + i},{y + d_{y} + j},{n + 1}} \right)}}}}}} & (1)\end{matrix}$

[0058] Here (x, y) is the position of the block, (d_(x), d_(y)) is amotion vector, n is the image number, N and M are the width and heightof the block, and Y(x,y,n) is the value of the luminance of a pixel atposition (x,y) in image n.

[0059] The variance parameter calculated by the second summation means108 corresponds to the VAR: sum of absolute luminance differencesbetween pixels in a particular block of an image, and the pixels of ablock in the same image but shifted a predetermined amount of pixels. Inorder to define the variance parameter, the function DIFF is introduced.$\begin{matrix}{{{DIFF}\left( {x_{1},y_{1},x_{2},y_{2},n_{1},n_{2}} \right)}:={\sum\limits_{i = 0}^{N}\quad {\sum\limits_{j = 0}^{M}\quad {{{Y\left( {{x_{1} + i},{y_{1} + j},n_{1}} \right)} - {Y\left( {{x_{2} + i},{y_{2} + j},n_{2}} \right)}}}}}} & (2)\end{matrix}$

[0060] where again N and M are the width and height of the block. Interms of this function, i.e. substitution of Equation (2) in Equation(1), the SAD becomes:

SAD(x,y,d _(x) ,d _(y) ,n):=DIFF(x,y,x +d _(x) ,y+d _(y) , n,n +1)  (3)

[0061] The variance parameter VAR can be defined as follows:

VAR(x,y):=½(DIFF(x,y,x+1,y,n,n)+DIFF(x,y,x,y+1,n,n))  (4)

[0062] This gives an expectation of the match error SAD for a motionvector error of 1 pixel. In this case the variance parameter is based onthe average over two different directions.

[0063] Another way to calculate a variance parameter is by taking intoaccount diagonal directions too: $\begin{matrix}{{{VAR4}\left( {x,y} \right)}:={\frac{1}{4}\begin{pmatrix}{{{DIFF}\left( {x,y,{x + 1},y,n,n} \right)} +} \\{{{DIFF}\left( {x,y,x,{y + 1},n,n} \right)} +} \\{{{{DIFF}\left( {x,y,{x + 1},{y + 1},n,n} \right)}/\sqrt{2}} +} \\{{{DIFF}\left( {x,y,{x + 1},{y + 1},n,n} \right)}/\sqrt{2}}\end{pmatrix}}} & (5)\end{matrix}$

[0064] Another way to calculate a variance parameter is by using largershifts, e.g. 2 pixels: $\begin{matrix}{{{VAR2}\left( {x,y} \right)}:={\frac{1}{2}\begin{pmatrix}{{\frac{1}{2}{{DIFF}\left( {x,y,{x + 2},y,n,n} \right)}} +} \\{{\frac{1}{2}{{DIFF}\left( {x,y,x,{y + 2},n,n} \right)}}\quad}\end{pmatrix}}} & (6)\end{matrix}$

[0065] The size of the shift is related to the block size. The VAR4measure is twice as expensive as the others. The VAR2 measure is lesssensitive to noise than VAR.

[0066]FIG. 2 schematically shows elements of an image processingapparatus 200 comprising:

[0067] receiving means 201 for receiving a signal representing images tobe displayed after some processing has been performed. The signal may bea broadcast signal received via an antenna or cable but may also be asignal from a storage device like a VCR (Video Cassette Recorder) orDigital Versatile Disk (DVD). The signal is provided at the inputconnector 206.

[0068] a motion estimator unit 100 as described in connection with FIG.1;

[0069] a motion compensated image processing unit 202; and

[0070] a display device 204 or displaying the processed images.

[0071] The motion compensated image processing unit 202 optionallysupports one or more of the following types of image processing:

[0072] De-interlacing: Interlacing is the common video broadcastprocedure for transmitting the odd or even numbered image linesalternately. De-interlacing attempts to restore the full verticalresolution, i.e. make odd and even lines available simultaneously foreach image;

[0073] Up-conversion: From a series of original input images a largerseries of output images is calculated. Output images are temporallylocated between two original input images; and

[0074] Temporal noise reduction.

[0075] The motion compensated image processing unit 202 requires imagesand motion vectors as its input.

[0076] Experiments have been carried out to investigate the relationbetween the variance parameter VAR, the match error SAD, and the motionvector error VE. Based on these experiments hypotheses are adoptedregarding these relations. First VE and VAR were taken as independentvariables, and SAD as dependent variable. Second SAD and VAR were takenas independent variables and VE as dependent variable. This latterrelation is of most relevance. It enables to qualify a motion vector andeven to quantify the motion vector error to certain extent. The resultis that an estimated motion vector can be categorized as follows:

[0077] The estimated motion vector is substantially equal to the truemotion vector: SAD<a*VAR with a predetermined small number.

[0078] The estimated motion vector deviates approximately e pixels fromthe true motion vector, i.e. the size of the motion vector errorapproximately equals e pixels. This can only be indicated for a range ofvalues of motion vector errors, e.g. 0≦e≦3 pixels: SAD>b,(3/5)SAD/VAR<e[pixels] with b some predetermined small threshold.

[0079] The estimated motion vector is substantially unequal to the truemotion vector: SAD>b, (3/5)SAD>3*VAR.

[0080] The difference between the estimated motion vector and the truemotion vector is unknown: remaining cases.

[0081] The set-up of the experiments was as follows. For a number ofsequences of images with simple motion, i.e. no or few occlusions, the3DRS motion estimator of the prior art was used to converge to highprecision, i.e. 0.25 pixel. To obtain sub-pixel accuracy bilinearinterpolation was used. The estimated motion vectors corresponded withthe “true” motion vectors. Then the “true” vectors were displaced by afixed amount in arbitrary directions. In other words the “true” motionvectors were adapted with the motion vector error. The values of SADcorresponding to the resulting motion vectors were then put in a2D-histogram against the value of VAR. It turned out, experimentally,that for fixed VE and VAR, the distribution of the SAD values could bewell approximated by a Gaussian distribution. This seemed to hold forarbitrary VAR, and for VE in the range of 0 to about 3 pixels. A blocksize of N=M=8 pixels was used. FIG. 3 shows a histogram of SAD valuesfor a test sequence used in the experiments for fixed VAR of 1500, andVE of 1 pixel, together with best Gaussian fit. Based on theexperimental results the hypothesis is adopted that for fixed VE andVAR, the distribution of the SAD values is well approximated by aGaussian distribution:

SAD≈

(μ(VAR,VE),σ(VAR,VE))  (7)

[0082] with the probability density function given by

Aexp(−(x−μ)²/2σ²)  (8)

[0083] The dependence of μ and σ on VAR and VE was analyzed by leastsquare fitting 1D slices of the 2D histogram to Gaussian curves. Theconstants of proportionality of the linear dependence of μ and σ on VAR,clearly depend on VE. These constants were estimated for VE=0, 0.5, 1,1.5, 2 and 3 pixels. It appeared that, in the range 0≦VE≦3 pixels, thedependence of μ as function of VAR is very nicely linear, whereas thedependence of σ on VAR starts deviating from a linear dependence when VEbecomes larger. A linear dependence still seems a reasonableapproximation up to about VE=3 pixels. For VE=0, the resultingmeasurements as a function of VAR have been plot in FIG. 4A and FIG. 4B.FIG. 4A shows the dependence of μ as function of VAR for VE=0, for atest sequence and FIG. 4B shows the dependence of σ as function of VARfor VE=0, for a test sequence. Note that the lines do not pass throughthe origin, corresponding to a residual SAD, even for VE=0. There areseveral reasons for this: vector errors in the “true” motion field, the0.25 pixel vector accuracy and sub-pixel interpolation errors, to namethree. FIG. 5A shows the dependence of μ as function of VAR for VE=1,for a test s equence and FIG. 5B shows the dependence of σ as functionof VAR for VE=1, for a test sequence.

[0084] In FIG. 6 the dependence of the constant of proportionality α inμ=αVAR on VE is shown. This dependence on VE is roughly linear in therange 0≦VE≦3 pixels. A least square fit yields:

μ=(0.7+1.5VE)VAR  (9)

σ=(0.2+0.5VE)VAR  (10)

[0085] This can be simplified to σ=μ/3.

[0086] Hence, a conditional probability distribution of SAD values givenVE and VAR has been derived. Given VAR and VE, the SAD is distributedaccording to the probability distribution $\begin{matrix}{{P\left( {{{SAD} = {\left. x \middle| {VE} \right. = e}},{{VAR} = v}} \right)} = {\frac{1}{\sqrt{2\pi}\sigma_{e,v}}{\exp \left( {{{- \left( {x - \mu_{e,v}} \right)^{2}}/2}\quad \sigma_{e,v}^{2}} \right)}}} & (11)\end{matrix}$

[0087] where μ_(e,v)=(0.7 +1.5e)v and σ_(e,v)=μ_(e,v) /3.0.

[0088] Above it is described how the SAD depends on the independentvariables VE and VAR. In a motion estimation unit according to theinvention SAD and VAR can be calculated directly. Next it will bedescribed how VE can be estimated based on SAD and VAR. Using Bayes'rule, the probability distribution of VE given SAD and VAR can becomputed: $\begin{matrix}{{P\left( {{{VE} = {\left. e \middle| {SAD} \right. = x}},{{VAR} = v}} \right)} = \frac{{P\left( {{{SAD} = {\left. x \middle| {VE} \right. = e}},{{VAR} = v}} \right)}{P\left( {{VE} = {\left. e \middle| {VAR} \right. = v}} \right)}}{P\left( {{SAD} = {\left. x \middle| {VAR} \right. = v}} \right)}} & (12)\end{matrix}$

[0089] Since nothing is known of the prior on the VE distribution, auniform distribution is chosen. The relevant range of pixels is from 0to 3 pixels, so P(VE=e|VAR=v)=1/3 if 0≦e≦3 and 0 otherwise. The resultsof the experiments indicated that the expected SAD scales linearly withVAR. Since nothing else is known of the prior, it is chosenP(SAD=x|VAR=v)=1/cv if 0≦x≦cv, and 0 otherwise, where c is an arbitraryrelatively large scaling factor. This yields to: $\begin{matrix}{{P\left( {{{VE} = {\left. e \middle| {SAD} \right. = x}},{{VAR} = v}} \right)} = {\frac{c}{3}\frac{v}{\sqrt{2\pi}\sigma_{e,v}}{\exp \left( {{{- \left( {x - \mu_{e,v}} \right)^{2}}/2}\sigma_{e,v}^{2}} \right)}}} & (13)\end{matrix}$

[0090] In FIG. 7 the probability density function has been plot forseveral values of x/v. The expectation value E(VE) and standarddeviation SD(VE) of VE according to these distributions are shown inTable 1. TABLE 1 SAD/VAR 0.5 1.0 2.0 3.0 4.0 5.0 E(VE) 0.4 0.5 1.2 1.92.5 3.0 SD(VE) 0.6 0.6 0.7 0.7 0.7 0.7

[0091] Across a useful range these results can be summarized by$\begin{matrix}{{E({VE})} \approx {\frac{3}{5}\frac{SAD}{VAR}}} & (14)\end{matrix}$

 SD(VE)≈0.7  (15)

[0092] With Equation (14) the expected value of a motion vector errorcan be estimated if the values of the variance parameter VAR and thematch error SAD are known. Equation (14) holds for the ranges asindicated in Table 1. Note that in practice one would add a small numberto VAR to make Equation (14) less sensitive to noise, and to avoiddivision by zero. The analysis sketched above has also been carried outfor the measures VAR4 and VAR2 mentioned above. The results were verysimilar.

[0093] It should be noted that the above-mentioned embodimentsillustrate rather than limit the invention and that those skilled in theart will be able to design alternative embodiments without departingfrom the scope of the appended claims. In the claims, any referencesigns placed between parentheses shall not be constructed as limitingthe claim. The word ‘comprising’ does not exclude the presence ofelements or steps not listed in a claim. The word “a” or “an” precedingan element does not exclude the presence of a plurality of suchelements. The invention can be implemented by means of hardwarecomprises several distinct elements and by means of a suitableprogrammed computer. In the unit claims enumerating several means,several of these means can be embodied by one and the same item ofhardware.

1. A motion estimation unit (100) for generating motion vectors, eachcorresponding to a respective segment (116) of a first image (118),comprising a first summation means (106) for calculating a match errorof a motion vector (126) of the segment (116) by summation of absolutedifferences between pixel values of the segment (116) and pixel valuesof a second image (120), characterized in further comprising: a secondsummation means (108) for calculating a variance parameter by summationof absolute differences between pixel values of the segment (116) andpixel values of the first image (118); and an estimation means (110) forestimating a motion vector error (130) by comparing the match error withthe variance parameter.
 2. A motion estimation unit (100) as claimed inclaim 1, characterized in that the second summation means (108) isdesigned to calculate the variance parameter by adding: absolutedifferences between pixel values of the segment (116) and pixel valuesof a second segment (119) which corresponds to the segment (116) beingshifted at least one pixel in a first direction; and absolutedifferences between pixel values of the segment (116) and pixel valuesof a third segment (115) which corresponds to the segment (116) beingshifted at least one pixel in a second direction, the first directionbeing cross to the second direction.
 3. A motion estimation unit (100)as claimed in claim 2, characterized in that the second summation means(108) is designed to add also absolute differences between pixel valuesof the segment (116) and pixel values of a fourth segment (117) whichcorresponds to the segment (116) being shifted at least one pixel in thefirst direction and at least one pixel in the second direction.
 4. Amotion estimation unit (100) as claimed in claim 1, characterized inbeing designed to generate the motion vector (126) of the segment (116),the segment (116) being a block of pixels.
 5. A motion estimation unit(100) as claimed in claim 1, characterized in being designed to generatethe motion vector (126) of the segment (116) based on luminance valuesas pixel values.
 6. A method of generating motion vectors, eachcorresponding to a respective segment (116) of a first image (118),comprising a first summation step of calculating a match error of amotion vector (126) of the segment (116) by summation of absolutedifferences between pixel values of the segment (116) and pixel valuesof a second image (120), characterized in further comprising: a secondsummation step of calculating a variance parameter by summation ofabsolute differences between pixel values of the segment (116) and pixelvalues of the first image (118); and an estimation step of estimating amotion vector error (130) by comparing the match error with the varianceparameter.
 7. A method as claimed in claim 6, characterized in that inthe second summation step the variance parameter is calculated byadding: absolute differences between pixel values of the segment (116)and pixel values of a second segment (119) which corresponds to thesegment (116) being shifted at least one pixel in a first direction; andabsolute differences between pixel values of the segment (116) and pixelvalues of a third segment (115) which corresponds to the segment (116)being shifted at least one pixel in a second direction, with the firstdirection cross to the second direction.
 8. A method as claimed in claim7, characterized in that in the second step are also added absolutedifferences between pixel values of the segment (116) and pixel valuesof a fourth segment (117) which corresponds to the segment (116) beingshifted at least one pixel in the first direction and at least one pixelin the second direction.
 9. An image processing apparatus (200)comprising: a motion estimation unit (100) for generating motionvectors, each corresponding to a respective segment (116) of a firstimage (118), comprising a first summation means (106) for calculating amatch error of a motion vector (126) of the segment (116) by summationof absolute differences between pixel values of the segment (116) andpixel values of a second image (120); and a motion compensated imageprocessing unit (202), characterized in that the motion estimation (100)unit further comprises: a second summation means (108) for calculating avariance parameter by summation of absolute differences between pixelvalues of the segment (116) and pixel values of the first image (118);and an estimation means (110) for estimating a motion vector error (130)by comparing the match error with the variance parameter.
 10. An imageprocessing apparatus (200) as claimed in claim 9, characterized in thatthe motion compensated image processing unit (202) is designed to reducenoise in the first image (118).
 11. An image processing apparatus (200)as claimed in claim 9, characterized in that the motion compensatedimage processing unit (202) is designed to de-interlace the first image(118).
 12. An image processing apparatus (200) as claimed in claim 9,characterized in that the motion compensated image processing unit (202)is designed to perform an up-conversion.